A Comparison of Two Classes of Methods for Estimating False Discovery Rates in Microarray Studies
The goal of many microarray studies is to identify genes that are differentially expressed between two classes or populations. Many data analysts choose to estimate the false discovery rate (FDR) associated with the list of genes declared differentially expressed. Estimating an FDR largely reduces t...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
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| Series: | Scientifica |
| Online Access: | http://dx.doi.org/10.6064/2012/519394 |
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| Summary: | The goal of many microarray studies is to identify genes that are differentially expressed between two classes or populations. Many data analysts choose to estimate the false discovery rate (FDR) associated with the list of genes declared differentially expressed. Estimating an FDR largely reduces to estimating π1, the proportion of differentially expressed genes among all analyzed genes. Estimating π1 is usually done through P-values, but computing P-values can be viewed as a nuisance and potentially problematic step. We evaluated methods for estimating π1 directly from test statistics, circumventing the need to compute P-values. We adapted existing methodology for estimating π1 from t- and z-statistics so that π1 could be estimated from other statistics. We compared the quality of these estimates to estimates generated by two established methods for estimating π1 from P-values. Overall, methods varied widely in bias and variability. The least biased and least variable estimates of π1, the proportion of differentially expressed genes, were produced by applying the “convest” mixture model method to P-values computed from a pooled permutation null distribution. Estimates computed directly from test statistics rather than P-values did not reliably perform well. |
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| ISSN: | 2090-908X |